Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detection

• URL: http://arxiv.org/abs/2512.17350v1
• Date: Fri, 19 Dec 2025 08:47:09 GMT
• Title: Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detection
• Authors: Chenming Zhou, Jiaan Wang, Yu Li, Lei Li, Juan Cao, Sheng Tang,
• Abstract summary: A critical limitation of current detectors is their failure to generalize to images from unseen generative models.<n>We introduce a simple yet remarkably effective pixel-level mapping pre-processing step to disrupt the pixel value distribution of images.<n>We show that our approach significantly boosts the cross-generator performance of state-of-the-art detectors.
• Score: 30.53429368921365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid evolution of generative technologies necessitates reliable methods for detecting AI-generated images. A critical limitation of current detectors is their failure to generalize to images from unseen generative models, as they often overfit to source-specific semantic cues rather than learning universal generative artifacts. To overcome this, we introduce a simple yet remarkably effective pixel-level mapping pre-processing step to disrupt the pixel value distribution of images and break the fragile, non-essential semantic patterns that detectors commonly exploit as shortcuts. This forces the detector to focus on more fundamental and generalizable high-frequency traces inherent to the image generation process. Through comprehensive experiments on GAN and diffusion-based generators, we show that our approach significantly boosts the cross-generator performance of state-of-the-art detectors. Extensive analysis further verifies our hypothesis that the disruption of semantic cues is the key to generalization.

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